Advances in portable medical devices and in electronic communications are enabling the remote monitoring of patients with many chronic conditions, including diabetes, hypertension, asthma, heart failure and chronic anticoagulation. As a result, clinicians will soon be inundated by hundreds of electronics results and messages every day. The clinician will no longer function in an assembly-line fashion, but will become more like a dispatcher or air-traffic controller, electronically monitoring many processes simultaneously. Clinicians will no longer ask simply, "How is Mrs. X?" They will also ask the computer "Among my 2000 patients, which ones need my attention today?" Neither clinicians, nor electronic medical records (EMR) systems, are prepared for this change. The goal of this project is to derive a set of design principles, demonstrated and evaluated in the context of specific systems, that helps future system developers (including ourselves) to construct tools for the management of large and complex data streams in a way that assure accurate, efficient, and timely detection of clinically relevant patterns, and that has a mode of use that is cognitively manageable. The specific aims are to: 1) develop a tagged dataset of glucose and blood pressure remote monitoring data that can be used for later development and evaluation studies; 2) analyze the statistical characteristics of the fingerstick glucose and blood pressure data from the Informatics for Diabetes Education and Telemedicine (IDEATel) project; 3) develop a set of candidate alerting mechanisms to automate the identification of clinically relevant patterns; 4) develop an interaction model for clinician review of remote monitoring data; 5) from this model, develop prototype user interfaces for presenting the data; and, 6) evaluate the performance of the interfaces and alerting strategies. The accomplishment of these aims will represent the initial steps with respect to the constructing and evaluating of prototype software to mediate between large complex data streams and health care providers.